{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['fmin']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
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       "        <td><a href=\"#\" onclick=\"$('#aDAkCHghTv').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>a</td>\n",
       "        <td>5</td>\n",
       "        <td>0.1</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>0</td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
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       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>b</td>\n",
       "        <td>5</td>\n",
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       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>0</td>\n",
       "        <td>10</td>\n",
       "        <td>No</td>\n",
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       "<pre id=\"aDAkCHghTv\" style=\"display:none;\">\n",
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       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & a & 5 & 0.1 &  &  & 0.0 &  & No\\\\\n",
       "\\hline\n",
       "1 & b & 5 & 0.1 &  &  & 0.0 & 10 & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "</textarea>\n",
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     "data": {
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       "<table>\n",
       "    <tr>\n",
       "        <td title=\"Minimum value of function\">FCN = 10.387013182671136</td>\n",
       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 64</td>\n",
       "        <td title=\"Number of call in last migrad\">NCALLS = 64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Estimated distance to minimum\">EDM = 1.9313890542083067e-06</td>\n",
       "        <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "        <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "        UP = 1.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "        <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "        <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "        <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "        <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "        <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "        <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
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       "    <tr>\n",
       "        <td><a href=\"#\" onclick=\"$('#LbWrvhFXGx').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>a</td>\n",
       "        <td>0.990894</td>\n",
       "        <td>0.0587697</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>0</td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
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       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>b</td>\n",
       "        <td>1.94501</td>\n",
       "        <td>0.0990814</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>0</td>\n",
       "        <td>10</td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<pre id=\"LbWrvhFXGx\" style=\"display:none;\">\n",
       "<textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\n",
       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & a & 0.990894 & 0.0587697 &  &  & 0.0 &  & No\\\\\n",
       "\\hline\n",
       "1 & b & 1.94501 & 0.0990814 &  &  & 0.0 & 10 & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
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       "</pre>"
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     "text": [
      "{'edm': 1.9313890542083067e-06,\n",
      " 'fval': 10.387013182671136,\n",
      " 'has_accurate_covar': True,\n",
      " 'has_covariance': True,\n",
      " 'has_made_posdef_covar': False,\n",
      " 'has_posdef_covar': True,\n",
      " 'has_reached_call_limit': False,\n",
      " 'has_valid_parameters': True,\n",
      " 'hesse_failed': False,\n",
      " 'is_above_max_edm': False,\n",
      " 'is_valid': True,\n",
      " 'nfcn': 64,\n",
      " 'up': 1.0}\n",
      "[{'error': 0.058769726729121397,\n",
      "  'has_limits': True,\n",
      "  'has_lower_limit': True,\n",
      "  'has_upper_limit': False,\n",
      "  'is_const': False,\n",
      "  'is_fixed': False,\n",
      "  'lower_limit': 0.0,\n",
      "  'name': 'a',\n",
      "  'number': 0,\n",
      "  'upper_limit': None,\n",
      "  'value': 0.9908935407181958},\n",
      " {'error': 0.09908144231339677,\n",
      "  'has_limits': True,\n",
      "  'has_lower_limit': True,\n",
      "  'has_upper_limit': True,\n",
      "  'is_const': False,\n",
      "  'is_fixed': False,\n",
      "  'lower_limit': 0.0,\n",
      "  'name': 'b',\n",
      "  'number': 1,\n",
      "  'upper_limit': 10.0,\n",
      "  'value': 1.9450148847762057}]\n"
     ]
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "from pprint import pprint\n",
    "from iminuit import Minuit\n",
    "\n",
    "def line(x,a,b):\n",
    "    return a+x*b\n",
    "\n",
    "data_x = linspace(0,1,10)\n",
    "# 预先从正态分布计算随机数\n",
    "offsets = array([-0.49783783, -0.33041722, -1.71800806, 1.60229399, 1.36682387,\n",
    "                 -1.15424221, -0.91425267, -0.03395604, -1.27611719, -0.7004073 ])\n",
    "data_y = line(data_x,1,2)+0.1*offsets  #生成具有随机偏移的数据点\n",
    "plot(data_x,data_y,'o')\n",
    "xlim(-0.1,1.1)\n",
    "\n",
    "# 用最小二乘法（least_squares）拟合直线\n",
    "# 计算对于给定a，b的 y偏差平方和\n",
    "def least_squares(a,b):\n",
    "    yvar = 0.01\n",
    "    return sum ((data_y-line(data_x,a,b))**2/yvar)\n",
    "\n",
    "m = Minuit(least_squares,a=5,b=5,error_a=0.1,error_b=0.1,\n",
    "           limit_a=(0,None),limit_b=(0,10),errordef=1)    \n",
    "    # 初始值 a=5,b=5; error命令即步长，均定为1\n",
    "    # errordef=1用于正确生成最小二乘函数的参数不确定性估计值\n",
    "    # 可以使用 m = Minuit(least_squares,pedantic=False)  直接使用默认值\n",
    "    # limit命令使a，b的参数范围被限制为 a大于0 b在0到10之间\n",
    "    # 可以通过fix命令使某个参数值固定，如 fix_a=True\n",
    "\n",
    "m.print_param() # 检查当前参数设定，会生成表格\n",
    "\n",
    "fmin,param = m.migrad()  # 拟合结果，生成表格\n",
    "pprint(fmin)   # 拟合的情况\n",
    "pprint(param)  # 拟合的结果 name参数名，value参数值，error参数的不确定性估计\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.298376647833892"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmin.fval/(len(data_y)-2)  #reduced chi2 理想情况下在1左右 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.387013182671136\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "pprint(m.fval)  # 最小值处的函数值\n",
    "pprint(m.migrad_ok()) # migrad结果是否有效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span>Minos status for a: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
       "        <td>-0.05870261580988936</td>\n",
       "        <td>0.05884843215595304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Validity of minos error\">Valid</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Did minos error search hit limit of any parameter?\">At Limit</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"New minimum found when doing minos scan.\">New Min</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span>Minos status for b: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
       "        <td>-0.09915706890347077</td>\n",
       "        <td>0.09901669881803556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Validity of minos error\">Valid</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Did minos error search hit limit of any parameter?\">At Limit</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"New minimum found when doing minos scan.\">New Min</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不确定性 置信区间\n",
    "param = m.minos()\n",
    "m.print_param()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1, 1.1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = m.np_values()\n",
    "ve = m.np_errors()\n",
    "vm = m.np_merrors()\n",
    "\n",
    "# 显示hesse误差\n",
    "errorbar((0,1),v,ve,fmt='ob')\n",
    "\n",
    "# 显示minos误差\n",
    "errorbar((0.01,1.01),v,vm,fmt='or')\n",
    "\n",
    "xlabel('parameter index')\n",
    "xticks((0,1))\n",
    "xlim(-0.1,1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.ContourSet at 0x2a5fc695278>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.draw_mncontour('a','b',nsigma=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
